Cannabis use is associated with lower rates of initiation of injection drug use among street‐involved youth: A longitudinal analysis
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Street-involved youth are known to be at elevated risk of initiating injection drug use. However, the impact of so-called 'gateway' drugs, such as cannabis, on injection initiation is unknown. The objective of this study was to examine the association between cannabis use and initiation of injection drug use among a prospective cohort of street-involved youth in Vancouver, Canada. DESIGN AND METHODS: Data for this study were collected from the At-Risk Youth Study. From September 2005 to May 2015, participants aged 14-26 who reported illicit drug use were recruited into this open prospective cohort study. An extended Cox regression model with time-updated covariates was used to identify factors independently associated with injection initiation. RESULTS: During the study period, 481 street-involved youth were included in this study. Of these, 228 (47.4%) reported at least daily cannabis use, and 103 (21.4%) initiated injection drug use. In a multivariable analysis, ≥daily cannabis use was associated with slower rates of injection initiation (adjusted relative hazard 0.66, 95% confidence interval 0.45-0.98; P = 0.038). Sub-analyses revealed that cannabis use was negatively associated with initiation of injection stimulants but not initiation of injection opioids. DISCUSSION AND CONCLUSIONS: Given the expansion of cannabis legalisation throughout North America, it is encouraging that cannabis use was associated with slower time to initiation of injection drug use in this cohort. This finding challenges the view of cannabis as a gateway substance that precipitates the progression to using harder and more addictive drugs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".